TY - JOUR
T1 - Data analysis for hot film-anemometry in turbulent bubbly flow
AU - Luther, S.
AU - Rensen, J.M.
AU - van den Berg, Th.H.
AU - Lohse, D.
PY - 2005
Y1 - 2005
N2 - We report on a new method for the analysis of hot-film anemometry in turbulent bubbly flows based on stochastic pattern recognition summarizing our results of [J.M. Rensen, S. Luther, J. de Vries, D. Lohse, Hot-film anemometry in bubbly flow I: bubble–probe interaction, in press; S. Luther, J. Rensen, Hot-film anemometry in bubbly flow II: local phase discrimination, in press; J.M. Rensen, S. Luther, D. Lohse, Velocity structure functions in turbulent two-phase flows, J. Fluid Mech., in press]. It consists of an optimal signal decomposition using an adaptive wavelet transform and neural network based classification. We discuss the application of autoregressive models to obtain energy spectra for gapped time series.
AB - We report on a new method for the analysis of hot-film anemometry in turbulent bubbly flows based on stochastic pattern recognition summarizing our results of [J.M. Rensen, S. Luther, J. de Vries, D. Lohse, Hot-film anemometry in bubbly flow I: bubble–probe interaction, in press; S. Luther, J. Rensen, Hot-film anemometry in bubbly flow II: local phase discrimination, in press; J.M. Rensen, S. Luther, D. Lohse, Velocity structure functions in turbulent two-phase flows, J. Fluid Mech., in press]. It consists of an optimal signal decomposition using an adaptive wavelet transform and neural network based classification. We discuss the application of autoregressive models to obtain energy spectra for gapped time series.
U2 - 10.1016/j.expthermflusci.2005.03.016
DO - 10.1016/j.expthermflusci.2005.03.016
M3 - Article
SN - 0894-1777
VL - 7
SP - 821
EP - 826
JO - Experimental thermal and fluid science
JF - Experimental thermal and fluid science
IS - 7
ER -